Benchmarking Tests: Benchmarks to Test
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@ndmasters is already working on this.
Since Aug 5, 2026.
enhancement
- Dominant language
- Python
- Stars
- 4
- Forks
- 1
- Avg merge
- 2d 5h
- Merged PRs (30d)
- 3
Description
There are many benchmarks to test. Decide on the minimum and expand from there. These are some idea below.
- Paper from https://github.com/Accenture/mcp-benchis located here https://arxiv.org/abs/2508.20453
- Maybe we can use this but haven’t looked too much into it
- See what the connections are since we already connect to the models
- Types of failures
- Called the right tool but with wrong parameters (parameters that don’t exist in the method)
- Didn’t call the right tool but provided correct parameters
- Called the right tool but interpreted parameters wrong (parameters extracted from sentence incorrectly e.g. passed 10 runs to lower bounds)
- Prompt didn’t provide all the necessary parameters
- Prompt provided more than required parameters
- Non-valid inputs e.g. only positives but gave it a negative
- Fix in mcp server to capture these out of bounds
- Update docstrings
- Looking for paths or files and they got updated
- Machine is down, resources aren't available
- Time out errors
- Model waiting for user input e.g. some string and then enter
- Create simple simulations to run faster
- Caught at the ci/cd time limit exceeded
- Gitlab duo
- Running simulation that takes longer than usual
| Field | Why |
|---|---|
| task_id | Ties to a specific scenario |
| tools_called | Trajectory for debugging ß we probably want to track the order here and maybe args provided as well |
| task_success | Binary pass/fail |
| llm_judge_score | Quality of final output (1-5) |
| total_tool_calls | Efficiency |
| total_latency_ms | Performance |
| model_version | Catch regressions across model updates |
| timestamp | Trend analysis over time |
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